About pavana
AI/ML Engineer with 4.5+ years of industry experience designing and deploying production-grade machine learning and Generative AI systems across finance and retail domains. I recently completed my Master of Science in Computer Science from California State University, Fullerton (July 2025), strengthening my foundation in advanced ML, data systems, and applied analytics.I specialize in Generative AI and Large Language Models (GPT-family, LLaMA, BERT), with hands-on experience building Retrieval-Augmented Generation (RAG) pipelines, LLM orchestration using LangChain, and parameter-efficient fine-tuning (LoRA, PEFT). My work focuses on turning unstructured data into reliable, auditable insights that can be safely used in enterprise environments.At Morgan Stanley, I contribute to enterprise-scale GenAI platforms for equity research and earnings intelligence, reducing research turnaround from multi-day cycles to same-day outputs while meeting strict AI governance, compliance, and explainability standards.Previously at Zensar Technologies, I built demand forecasting, pricing optimization, recommendation, and computer vision systems for large retail and pharmacy clients, delivering measurable impact including $130K in annual cost savings and significant reductions in manual operations.Core focus areas: * Generative AI & LLMs (RAG, LangChain) * Machine learning & deep learning (XGBoost, LSTM, PyTorch, TensorFlow) * MLOps / LLMOps and production ML systems * Data-driven solutions for finance and retailOpen to high-impact AI/ML roles where rigor, scale, and real-world outcomes matter.
Key Skills
Experience
Ai / Ml Engineer
CurrentMorgan Stanley
Designed and deployed enterprise Generative AI platforms using GPT-family and LLaMA models for equity research and earnings intelligence, reducing research turnaround from multi-day cycles to same-day outputs. -Built LLM orchestration workflows using LangChain, integrating market data feeds, SQL/NoSQL stores, REST APIs, and feature stores to automate investment summaries and scenario narratives. -Implemented Retrieval-Augmented Generation (RAG) systems with vector embeddings and semantic search across research notes and filings, eliminating hundreds of repetitive analyst queries weekly. -Developed and productionized PyTorch-based ML models for market signal classification, factor ranking, and regime detection, achieving a precision lift of 0.11 in quantitative pipelines. -Applied parameter-efficient fine-tuning techniques (LoRA, PEFT, adapters) on proprietary financial text, saving 30–35 analyst hours per week. -Built end-to-end MLOps / LLMOps pipelines with CI/CD, model versioning, automated evaluation, drift detection, and prompt regression testing. -Collaborated with research, risk, legal, and compliance teams to establish AI governance frameworks ensuring explainability, audit trails, and data lineage.
Undergraduate Research Assistant
Dayananda Sagar University
* Collected and analyzed agricultural sensor data to improve accuracy in automated seed placement. * Applied machine learning and image processing to monitor field conditions using real-time IoT data. * Presented insights through dashboards and technical documentation to support precision farming outcomes.
Machine Learning Engineer
Zensar Technologies
* Built store-level demand forecasting models using XGBoost, Prophet, and LightGBM across 70 stores and 1.1M SKUs, generating $130K in annual cost savings. * Developed customer purchase propensity, basket-affinity, and churn prediction models, enabling personalized promotions and recovering 68K repeat visits per quarter. * Designed pricing and markdown optimization models using time-series and elasticity analysis, processing 1,200 pricing scenarios weekly. * Implemented computer vision pipelines with TensorFlow and OpenCV to automate shelf audits and planogram compliance, saving 1,000+ manual hours per quarter. * Deployed large-scale ML pipelines using Apache Spark, Airflow, and AWS, supporting 400K+ weekly records. * Delivered executive dashboards in Power BI and Snowflake tracking 160+ operational KPIs.
Education
California State University, Fullerton
Masters
California State University, Fullerton
Master Of Science
Dayananda Sagar University
Bachelor Of Technology
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Common Questions
What is pavana's expertise?
pavana specializes in Fine-Tuning Engineer, with expertise in algorithms, apache kafka, apache spark, boot camp, c++.
Where is pavana located?
pavana is based in fullerton, california, united states.
How much experience does pavana have?
pavana has 6+ years of professional experience.
How can I contact pavana?
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